Quadratic Discriminant Analysis (QDA) is a classification technique used when the assumption of equal covariance across classes in Linear Discriminant Analysis is violated, allowing for more flexible decision boundaries. It models each class with its own covariance matrix, enabling it to capture the variance within classes more accurately, but at the cost of increased model complexity and risk of overfitting with small datasets.